{"id":"https://openalex.org/W7126088176","doi":"https://doi.org/10.1109/bibm66473.2025.11357017","title":"DBA-Net: Dynamic Boundary-Aware Network for 3D Medical Point Cloud Segmentation","display_name":"DBA-Net: Dynamic Boundary-Aware Network for 3D Medical Point Cloud Segmentation","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126088176","doi":"https://doi.org/10.1109/bibm66473.2025.11357017"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11357017","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124201763","display_name":"Song Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Song Zhao","raw_affiliation_strings":["School of Information Science and Engineering, Yunnan University,Kunming,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yunnan University,Kunming,China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023722190","display_name":"YaLan Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yalan Ye","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072311459","display_name":"Shuhua Wang","orcid":"https://orcid.org/0000-0002-0736-3010"},"institutions":[{"id":"https://openalex.org/I106994412","display_name":"Sinopec (China)","ror":"https://ror.org/0161q6d74","country_code":"CN","type":"company","lineage":["https://openalex.org/I106994412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuhua Wang","raw_affiliation_strings":["Exploration and Development Research Institute, Shengli Oilfield Company, SINOPEC,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Exploration and Development Research Institute, Shengli Oilfield Company, SINOPEC,China","institution_ids":["https://openalex.org/I106994412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124192611","display_name":"Xiaobing Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaobing Zhou","raw_affiliation_strings":["School of Information Science and Engineering, Yunnan University,Kunming,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yunnan University,Kunming,China","institution_ids":["https://openalex.org/I189210763"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72346966,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4497","last_page":"4502"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.7039999961853027,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.7039999961853027,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.11100000143051147,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.02630000002682209,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7056000232696533},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7052000164985657},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.6891999840736389},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.573199987411499},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46230000257492065},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45010000467300415},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.448199987411499},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4390999972820282}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7056000232696533},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7052000164985657},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.6891999840736389},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6087999939918518},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.573199987411499},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5482000112533569},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46230000257492065},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45010000467300415},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.448199987411499},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4390999972820282},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4099999964237213},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3903999924659729},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.32749998569488525},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3174000084400177},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3142000138759613},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2711000144481659},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C2778775528","wikidata":"https://www.wikidata.org/wiki/Q5135432","display_name":"Closing (real estate)","level":2,"score":0.2517000138759613},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11357017","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6689008474349976,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G2568803418","display_name":null,"funder_award_id":"U2333211","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2963125977","https://openalex.org/W2963231572","https://openalex.org/W2979750740","https://openalex.org/W2981669902","https://openalex.org/W2990613095","https://openalex.org/W3010074503","https://openalex.org/W3035168834","https://openalex.org/W3107518100","https://openalex.org/W3111535274","https://openalex.org/W3170019900","https://openalex.org/W3171433839","https://openalex.org/W3174926460","https://openalex.org/W4214755140","https://openalex.org/W4226106508","https://openalex.org/W4295937531","https://openalex.org/W4308621049","https://openalex.org/W4312775058","https://openalex.org/W4362683575","https://openalex.org/W4383220296","https://openalex.org/W4385976449","https://openalex.org/W4390873929","https://openalex.org/W4396877769","https://openalex.org/W4413145507"],"related_works":[],"abstract_inverted_index":{"Medical":[0],"point":[1,17],"cloud":[2],"segmentation":[3,158],"accuracy":[4],"is":[5],"often":[6],"limited":[7],"by":[8],"feature":[9],"confusion":[10],"in":[11,153],"boundary":[12,59,154],"regions,":[13],"which":[14,47],"arises":[15],"from":[16],"sparsity,":[18],"shape":[19],"complexity,":[20],"and":[21,37,58,99,126,138,146,156],"structural":[22],"similarity.":[23],"To":[24],"address":[25],"this,":[26],"we":[27],"introduce":[28],"a":[29,49,62,93,121,127],"boundary-aware":[30],"perspective":[31],"that":[32],"categorizes":[33],"boundaries":[34],"into":[35],"inner":[36,109],"outer":[38,80],"types.":[39],"We":[40],"propose":[41],"the":[42,82,111,144],"Dynamic":[43,128],"Boundary-Aware":[44],"Network":[45],"(DBA-Net),":[46],"employs":[48],"Boundary-aware":[50],"Dual":[51],"Stream":[52],"(BDS)":[53],"module":[54],"to":[55],"decouple":[56],"semantic":[57],"features":[60],"via":[61],"Cross-Stream":[63],"Attention":[64],"Module":[65],"(CSAM),":[66],"alongside":[67],"an":[68,100],"Adaptive":[69,85],"Boundary":[70,84,113],"Pseudo-Label":[71],"Calculation":[72],"(ABP-LC)":[73],"strategy":[74,98,125],"for":[75],"adaptive":[76],"label":[77],"generation.":[78],"For":[79,108],"boundaries,":[81,110],"Outer":[83],"Contextual":[86],"Discrepancy-guided":[87],"Graph":[88],"Convolution":[89],"(OACD-GC)":[90],"module,":[91,119],"incorporating":[92],"Soft":[94],"Edge":[95],"Connection":[96],"(SEC)":[97],"Iterative":[101],"Optimization":[102],"Mechanism":[103],"(IOM),":[104],"enhances":[105],"inter-class":[106,139],"discrimination.":[107],"Inner":[112],"Dy-namic":[114],"Supervised":[115],"Contrastive":[116],"Enhancement":[117],"(ID-SCE)":[118],"utilizing":[120],"Multi-Positive":[122],"Sample":[123,131],"(MPS)":[124],"Hard":[129],"Negative":[130],"Update":[132],"(DHNU)":[133],"mechanism,":[134],"improves":[135],"intra-class":[136],"aggregation":[137],"differentiation.":[140],"Extensive":[141],"experiments":[142],"on":[143],"IntrA":[145],"3DTeethSeg":[147],"datasets":[148],"demonstrate":[149],"DBA-Net's":[150],"superior":[151],"performance":[152],"recognition":[155],"overall":[157],"accuracy.":[159]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-01-30T00:00:00"}
